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---
language: en
license: mit
model_details: "\n ## Abstract\n This model, 'distilbert-finetuned-uncased',\
\ is a question-answering chatbot trained on the SQuAD dataset, demonstrating competency\
\ in building conversational AI using recent advances in natural language processing.\
\ It utilizes a BERT model fine-tuned for extractive question answering.\n\n \
\ ## Data Collection and Preprocessing\n The model was trained on the\
\ Stanford Question Answering Dataset (SQuAD), which contains over 100,000 question-answer\
\ pairs based on Wikipedia articles. The data preprocessing involved tokenizing\
\ context paragraphs and questions, truncating sequences to fit BERT's max length,\
\ and adding special tokens to mark question and paragraph segments.\n\n \
\ ## Model Architecture and Training\n The architecture is based on the BERT\
\ transformer model, which was pretrained on large unlabeled text corpora. For this\
\ project, the BERT base model was fine-tuned on SQuAD for extractive question answering,\
\ with additional output layers for predicting the start and end indices of the\
\ answer span.\n\n ## SQuAD 2.0 Dataset\n SQuAD 2.0 combines the existing\
\ SQuAD data with over 50,000 unanswerable questions written adversarially by crowdworkers\
\ to look similar to answerable ones. This version of the dataset challenges models\
\ to not only produce answers when possible but also determine when no answer is\
\ supported by the paragraph and abstain from answering.\n "
intended_use: "\n - Answering questions from the squad_v2 dataset.\n \
\ - Developing question-answering systems within the scope of the aai520-project.\n\
\ - Research and experimentation in the NLP question-answering domain.\n\
\ "
limitations_and_bias: "\n The model inherits limitations and biases from the\
\ 'distilbert-base-uncased' model, as it was trained on the same foundational data.\n\
\ It may underperform on questions that are ambiguous or too far outside\
\ the scope of the topics covered in the squad_v2 dataset.\n Additionally,\
\ the model may reflect societal biases present in its training data.\n "
ethical_considerations: "\n This model should not be used for making critical\
\ decisions without human oversight,\n as it can generate incorrect or biased\
\ answers, especially for topics not covered in the training data.\n Users\
\ should also consider the ethical implications of using AI in decision-making processes\
\ and the potential for perpetuating biases.\n "
evaluation: "\n The model was evaluated on the squad_v2 dataset using various\
\ metrics. These metrics, along with their corresponding scores,\n are detailed\
\ in the 'eval_results' section. The evaluation process ensured a comprehensive\
\ assessment of the model's performance\n in question-answering scenarios.\n\
\ "
training: "\n The model was trained over 10 epochs with a learning rate of\
\ 2e-05, using a batch size of 64.\n The training utilized a cross-entropy\
\ loss function and the AdamW optimizer, with gradient accumulation over 4 steps.\n\
\ "
tips_and_tricks: "\n For optimal performance, questions should be clear, concise,\
\ and grammatically correct.\n The model performs best on questions related\
\ to topics covered in the squad_v2 dataset.\n It is advisable to pre-process\
\ text for consistency in encoding and punctuation, and to manage expectations for\
\ questions on topics outside the training data.\n "
model-index:
- name: distilbert-finetuned-uncased
results:
- task:
type: question-answering
dataset:
name: SQuAD v2
type: squad_v2
metrics:
- type: Exact
value: 100.0
- type: F1
value: 100.0
- type: Total
value: 2
- type: Hasans Exact
value: 100.0
- type: Hasans F1
value: 100.0
- type: Hasans Total
value: 2
- type: Best Exact
value: 100.0
- type: Best Exact Thresh
value: 0.967875599861145
- type: Best F1
value: 100.0
- type: Best F1 Thresh
value: 0.967875599861145
- type: Total Time In Seconds
value: 0.03511825300000737
- type: Samples Per Second
value: 56.9504411281387
- type: Latency In Seconds
value: 0.017559126500003686
---
# Model Card for Model ID
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## Model Details
### Model Description
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- **Language(s) (NLP):** en
- **License:** mit
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## Uses
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### Direct Use
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Training Details
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## Evaluation
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#### Summary
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Technical Specifications [optional]
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